Advancing virtual primary care for people with opioid use disorder (VPC OUD): a mixed-methods study protocol
Bibliographic record
Abstract
INTRODUCTION: The emergence of COVID-19 introduced a dual public health emergency in British Columbia, which was already in the fourth year of its opioid-related overdose crisis. The public health response to COVID-19 must explicitly consider the unique needs of, and impacts on, communities experiencing marginalisation including people with opioid use disorder (PWOUD). The broad move to virtual forms of primary care, for example, may result in changes to healthcare access, delivery of opioid agonist therapies or fluctuations in co-occurring health problems that are prevalent in this population. The goal of this mixed-methods study is to characterise changes to primary care access and patient outcomes following the rapid introduction of virtual care for PWOUD. METHODS AND ANALYSIS: We will use a fully integrated mixed-methods design comprised of three components: (a) qualitative interviews with family physicians and PWOUD to document experiences with delivering and accessing virtual visits, respectively; (b) quantitative analysis of linked, population-based administrative data to describe the uptake of virtual care, its impact on access to services and downstream outcomes for PWOUD; and (c) facilitated deliberative dialogues to co-create educational resources for family physicians, PWOUD and policymakers that promote equitable access to high-quality virtual primary care for this population. ETHICS AND DISSEMINATION: Approval for this study has been granted by Research Ethics British Columbia. We will convene PWOUD and family physicians for deliberative dialogues to co-create educational materials and policy recommendations based on our findings. We will also disseminate findings via traditional academic outputs such as conferences and peer-reviewed publications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.067 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".